3 IntroductionMotivationCustomers shop online, from their homes, without any human interaction involved.Catalogs of online shops are so big and with so many continuous updates that no human, however expert, can effectively comprehend the space of available products.Use a flowchart asks the shopper a question, and the sequence of answers leads the shopper to the suggested shopping option.

4 IntroductionSHOPPINGADVISOR is a novel recommender system that helps users in shopping for technical products.car

5 IntroductionSHOPPINGADVISOR generates a tree-shaped flowchart, in which the internal nodes of the tree contain questions involve only attributes from the user space.non-expert users can understand easily.

6 Introduction Find the best user attribute to ask at each node.How to learn the structure of the tree, i.e., which questions to ask at each node.Find the best user attribute to ask at each node.This paper focus on identifying the attribute of interest, and not on the task of formulating the question in a human interpretable way.How to produce a suitable ranking at each node.Learning-to-rank approach

16 Rank list RANKSVM Count payoffproductABDC.FERank listRANKSVMCount payoffConsider all possible user attributes 𝛼, and choose as splitter the one that maximizes the pay-off.

17 Stopping criterion Grow the tree to its “entirety” Post-pruningIf a node’s child node is split by the “near-synonomous” tag trim the child nodeExample:travelvacationEmploy pruning rules on the validation set.

24 ConclusionProposed a novel recommender system, SHOPPINGADVISOR, that helps users to shop for technical products.SHOPPINGADVISOR leverages both user preferences and technical product attributes in order to generate its suggestions.At each node, SHOPPINGADVISOR suggests a ranking of products matching the preferences of the user.Compared with a baseline, and demonstrated the effectiveness of the approach.